Intended Audience: Researchers and Engineers working in the field of sensor networks
and fusion applications.
Description: Networking multiple sensors can provide an enhanced picture of an environment compared
to using individual sensors. Fusing information from these networked sensors, however,
challenge the conventional approaches in terms of scalability and resource awareness.
Distributed processing paradigms aim to address these challenges and provide a scalable,
robust and flexible computational structure by distributing the global tasks onto the network.
fusion algorithms, in a broad sense, aim to integrate the information exchanged
between the nodes for enhancing the situation awareness.
fusion algorithms, however, rely on exact sensor calibration and do not
account for errors in the parameters used to register the information from different sensors
onto a common coordinate frame. Imperfect knowledge of these parameters can induce
systematic errors and undermine the benefits of networked sensing. Therefore, calibration
using sensor data is a very desirable capability.
This tutorial will present methods for fusion and registration in networks of sensors. In the
first part, the focus will be on integrating information output by local filtering at the sensor
nodes. Both optimal and suboptimal
algorithms will be presented and discussed. The
second part will cover registration/calibration of sensors. First, a centralised setting will be
considered in which the sensor measurements are available at a centre. It will be shown how
the registration process can exploit the Probability Hypothesis Density (PHD) filtering
principles for handling the uncertainties in the multitarget
model. The second topic will be a
distributed setting in which several sensor nodes exchange filtered distributions as opposed
to measurements. A recent solution will be introduced which feature local processing at the
sensor nodes and message passing operations for selfcalibration.
Prerequisites: Target tracking, Bayesian filtering.
Presenter: Murat Uney, Simon Julier, and Daniel Clark
Murat Uney is a Research Fellow in the School of Engineering, University of Edinburgh. His
research interests are in the broad scope of statistical signal and information processing with
a particular emphasis on distributed, multimodal
and resource constrained problem settings,
and sensor fusion applications. He has industrial research and development experience in
both aerospace/defence and telecommunication sectors. He gave a tutorial on distributed
fusion algorithms in 2013 Summer School on Finite Set Statistics in
Edinburgh. These algorithms were demonstrated online
in a maritime environment, in
collaboration with BAE Systems Advanced Technology Centre and University College
London, and the results were presented in UDRC Summer School 2014 in Edinburgh. He
covered optimal and adaptive filtering in the context of statistical signal processing in UDRC
Summer School 2015, Surrey. His recent research focuses on sensor calibration in a
distributed latent parameter estimation setting.
Simon Julier is a Reader in the Department of Computer Science at UCL. In 2014, he was a
Distinguished Lecturer for the Aerospace & Electronic Systems Society. Before joining UCL,
Dr Julier worked for nine years at the 3D Mixed and Virtual Environments Laboratory
(3DMVEL) at the Naval Research Laboratory in Washington DC. From 2005 to 2007, he was
the Principal Investigator of the ONRfunded
Scalable Distributed Data Fusion Project
($700k) to develop techniques for robust fusion of multiple sources of data. Between 1999
and 2005 he was the Principal Investigator of the ONR funded Battlefield Augmented Reality
System ($5.2M), a research effort to develop manwearable
systems for providing situation
awareness information. He focused on problems relating to information filtering, tracking and
alignment, and error adaptation. He also served as the Associate Director of the 3DMVEL
He was cochair
of the IEEE VR 2006 and IEEE VR 2007 conferences. He
received a DPhil in robotics from the Robotics Research Group, Oxford University, UK.
Daniel Clark is an Associate Professor in Sensors and Systems at HeriotWatt
His research interests are in the development of the theory and applications of multiobject
estimation algorithms for sensor fusion problems. He was chair of the 2013 Summer School
on Finite Set Statistics in Edinburgh (with Dstl UDRC sponsorship) and Albuquerque (with
AFOSR sponsorship).He has collaborated closely with Dstl in the UK on a number of
projects in multitarget
tracking spanning theoretical algorithm development to practical
deployment in collaboration with BAE Systems, Finnmechanica, Thales, and DCNS. He
lectures mathematics to undergraduate electrical engineers and developed a course on
Fusion and Tracking” for a European Masters programme (Vibot). In 2014, he
was a Visiting Professor at the University of Colorado where he gave a lecture course on
estimation. He gave a tutorial in 2011 at ICASSP with Branko Ristic entitled
“Particle filters for multiobject
Bayes filtering and sensor control in the framework of random